Abstract
In this paper, we re-generated and implemented EURO NCAP standard scenarios for autonomous emergency braking using two Leddar M16 sensors. Out of total sixteen channels available in M16 module, we have used middle channels since only data of longitudinal axis was required for selected scenarios. The data is collected by mounting the sensors on hood of ego vehicle. We have referred constant values of multiple parameters in the collision and stopping time calculations from standard parameters defined in ADAS simulation toolbox of MATLAB 2018b. Data update and predicts of two datasets from Leddar M16 sensor is carried out using Kalman Filter algorithm considering one sensor data as ground truth data. Finally the final distance data output, from updated datasets are used to display autonomous emergency braking and forward collision warning over the screen of python console. According to decelerations at different stages, partial and full braking times are calculated.
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References
Kumar, R.S., Stanley, P.K., Gandhi, A.S.: Raspberry Pi based vehicle collision avoidance system. In: Proceedings IEEE International Conference on Innovations in Electrical, Electronics, Instrumentation and Media Technology, ICIEEIMT 2017, pp. 211–215, January 2017. https://doi.org/10.1109/ICIEEIMT.2017.8116838
Schmitt, A.: Europe Will Use Vehicle Tech to Prevent Speeding, Save Thousands of Lives (2019). https://usa.streetsblog.org/2019/03/05/europe-will-use-vehicle-tech-to-prevent-speeding-save-thousands-of-lives/
Ariyanto, M., Haryadi, G.D., Munadi, M., et al.: Development of low-cost autonomous emergency braking system (AEBS) for an electric car. In: Proceeding - 2018 5th International Conference on Electric Vehicular Technology, ICEVT 2018, pp. 167–171 (2019). https://doi.org/10.1109/ICEVT.2018.8628442
Emani, S., Soman, K.P., Sajith, V.V., Adarsh, S.: Obstacle detection and distance estimation for autonomous electric vehicle using stereo vision and DNN. In: Proceedings of ICSCSP 2018, vol. 2 (2019). https://doi.org/10.1007/978-981-13-3393-4_65
Van Ratingen, M., Williams, A., Lie, A., et al.: The European new car assessment programme: a historical review. Chin. J. Traumatol. 19, 63–69 (2016). https://doi.org/10.1016/j.cjtee.2015.11.016. English Ed
Kaempchen, N., Schiele, B., Dietmayer, K.: Situation assessment of an autonomous emergency brake for arbitrary vehicle-to-vehicle collision scenarios. IEEE Trans. Intell. Transp. Syst. 10, 678–687 (2009). https://doi.org/10.1109/TITS.2009.2026452
Welch, G., Bishop, G.: An Introduction to the Kalman Filter, vol. 7, pp. 1–16. University of North Carolina, Chapel Hill (2006). https://doi.org/10.1.1.117.6808
Kapse, R.: Implementing an Autonomous Emergency Braking with Simulink using two Radar Sensors (2019)
Binoy, B.N., Keerthana, T.: A GSM-based versatile unmanned ground vehicle. In: International Conference on “Emerging Trends in Robotics and Communication Technologies”, INTERACT-2010, Chennai, pp. 356–361 (2010)
Driving Scenario Generation and Sensor Models - MATLAB & Simulink - MathWorks India. https://in.mathworks.com/help/driving/driving-scenario-generation-and-sensor-models.html. Accessed 18 Apr 2019
Hulshof, W., Knight, I., Edwards, A., et al.: Autonomous emergency braking test results. In: Proceedings 23rd International Technical Conference on Enhanced Safety of Vehicles, pp. 1–13 (2013). https://doi.org/13-0168
Wallner, J., Tang, T., Lienkamp, M.: Development of an emergency braking system for teleoperated vehicles based on lidar sensor data. In: 2014 11th International Conference on Informatics in Control, Automation and Robotics, vol. 02, pp. 569–576 (2014). https://doi.org/10.5220/0005114905690576
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Kapse, R., Adarsh, S. (2020). Implementation of European NCAP Standard Autonomous Emergency Braking Scenarios Using Two Leddar M16 Sensors. In: Pandian, A., Ntalianis, K., Palanisamy, R. (eds) Intelligent Computing, Information and Control Systems. ICICCS 2019. Advances in Intelligent Systems and Computing, vol 1039. Springer, Cham. https://doi.org/10.1007/978-3-030-30465-2_53
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DOI: https://doi.org/10.1007/978-3-030-30465-2_53
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